Recommending Personalized Mobile Advertisement Based on Category Information of Mobile Applications
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Personalization has been identified as a key predictor for adopting mobile advertisements. This study proposes a new mechanism for recommending personalized mobile advertisements based on demographic information and purchase intention. What people installed on their mobile phones is likely to reflect who they are in terms of age and gender. The proposed method includes two processes: a user-modeling process to design the recommendation algorithm based on the users’ demographic profiles and purchase intention, and a service process to recommend personalized mobile advertisements to new users. To test the proposed method, three hypotheses were constructed and evaluated: 1) Users’ demographic profiles (i.e., age and gender) can be inferred from the category information on the installed mobile apps; 2) The proposed method shows 70% or higher accuracy in matching the category of the product list that a new user has purchase intention; 3) The proposed method reflects users’ purchase intention more accurately than the random method. The hypotheses were tested with 71 Korean users and their 3,401 mobile applications through machine learning. Overall, the results indicated that users’ gender and age information can be inferred from the app category information, and the proposed personalized method showed 38.4% better accuracy for matching users’ purchase intention than the random recommendation method.
Abstract
Personalization has been identified as a key predictor for adopting mobile advertisements. This study proposes a new mechanism for recommending personalized mobile advertisements based on demographic information and purchase intention. What people installed on their mobile phones is likely to reflect who they are in terms of age and gender. The proposed method includes two processes: a user-modeling process to design the recommendation algorithm based on the users’ demographic profiles and purchase intention, and a service process to recommend personalized mobile advertisements to new users. To test the proposed method, three hypotheses were constructed and evaluated: 1) Users’ demographic profiles (i.e., age and gender) can be inferred from the category information on the installed mobile apps; 2) The proposed method shows 70% or higher accuracy in matching the category of the product list that a new user has purchase intention; 3) The proposed method reflects users’ purchase intention more accurately than the random method. The hypotheses were tested with 71 Korean users and their 3,401 mobile applications through machine learning. Overall, the results indicated that users’ gender and age information can be inferred from the app category information, and the proposed personalized method showed 38.4% better accuracy for matching users’ purchase intention than the random recommendation method.
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